Infrastructure managers are in charge of ensuring that infrastructure provides an acceptable level of
service to society. To evaluate whether such a level of service can be maintained in the case of natural
disasters, a generic risk assessment process was developed in the INFRARISK project. This process
accounts for the complex interactions between a multitude of spatio-temporal systems, which may
ultimately lead to negative impacts on society due to impairment of the infrastructure networks. Implementations
of this process rely heavily on computer support to perform a large number of natural
disaster simulations. The vast volume of resulting data additionally undergoes several aggregation
steps, which leads to a high variety of different data classes, each with unique characteristics.
The overall goal of this thesis is the development of visualization methods that enable an efficient
analysis of the data generated during such risk assessments. For this purpose, a simulation environment
was engineered to allow the execution of a multitude of simulations to investigate changes in the evolution
of the modeled systems due to different input parameters. The simulation results are automatically
postprocessed to yield several risk measures. Visualization methods described in the literature
and suitable for the analysis of each of these data classes were determined. In two cases, no appropriate
visualization method could be found. To fill these gaps, the state dependency graph (SDG) and the
rendering-based analysis techniques were developed.
The SDG facilitates the understanding of single simulations by representing each generated state of a
system as a node and the dependencies between these states as edges. Encoding state information in
the visual variables of the nodes and enriching the edges with impact information, such as maps depicting
the location of a landslide, allow to easily comprehend the evolution of the represented systems,
determine how impacts propagate through them, and compare the outcomes of different simulations.
This technique is particularly useful when used with an interactive map in which the geospatial representation
of a state of interest is displayed.
Including maps in a visual analysis tool requires a powerful rendering engine that allows to navigate
along time-series of data in real-time. Hence, the concept of rendering-based geospatial analysis is
investigated. This approach allows to undertake typical geospatial operations as part of the GPU-accelerated
rendering pipeline interactively, which is advantageous when dealing with a large volume
of geospatial datasets. Because of the high speed at which these operations are conducted, the resulting
datasets do not need to be physically stored, but can be immediately recomputed when required. This
is useful when comparing different states by generating difference maps, for example, or when computing
distance fields and buffers to obtain proximity information. In addition, the effects of parameter
changes for many methods such as kernel density estimation and inverse distance weighting can immediately
be investigated for multiple states. This simplifies the validation of the chosen parameters.
The developed methods were integrated into a prototypical visualization tool that was successfully
applied to risk assessment results of the INFRARISK project for the region of Chur, SwitzerlandShow more